Educational intentions, cognitive skills and earnings

Educational intentions, cognitive skills
and earnings expectations of French undergraduates
Claire Bonnard1, Jean-François Giret2, Marielle Lambert-Le Mener3
1
CLERSE, University of Lille 1, CNRS and IREDU, University of Burgundy,
[email protected]
2
IREDU, University of Burgundy, CNRS, [email protected]
3
IREDU, University of Burgundy, CNRS, [email protected]
December 15, 2012
Abstract
This paper aims to study the earnings expectations of first-year students at a French
university and to compare them with the observed earnings of young people in the labour
market for that same year. Our findings highlight the importance of the environment in which
students make their choices about education. Expected earnings are proportionally higher
when their parents seem to be involved in the careers guidance, even controlling for the effect
of parental socio-economic status. The positive opinion of parents about the orientation or the
connection between the discipline and the father’s occupation are generally associated with
higher earnings. In addition, our results show a strong impact of cognitive variables which
are far more significant than variables relating to past educational performances.
Keywords: Wage expectations, cognitive skills, first year students
JEL Classification codes: J30, J24
Introduction
The future level of earnings that young people still in education expect to attain is central to
decision-making in the theory of human capital investment. It determines their choices as to
their fields of study and so their individual levels of human capital. When attempting to
analyse interactions between educational supply and demand for certain occupations, as
proposed for example by Freeman in cobweb models, the way in which the earnings
expectations of pupils and students are formed is decisive in the demand for education, even if
their forecasts suffer from some degree of short-sightedness. Underestimating actual wages
may mean students do not pursue their studies as far as they might otherwise do or that they
do not opt for certain fields of study although they are remunerative on the labour market.
Students may also devote less time to their education by opting to take up a paid activity that
competes for their study time. As Jerrim (2011) points out, the implications of overestimating
earnings may be less detrimental to investment in education. For one thing, students put more
1
effort into their studies insofar as they expect higher returns, thereby reducing the probability
of failure and of drop-out. For another thing, if students continue to overestimate wages
through to the end of their education, their wage demands may lead tobetter matching on the
labour market, in accordance with job-searchtheory. A higher reserve wage would arguably
prompt job-seekers to be more alert to the employment offered to them. Conversely, an
overestimation may also lead young people in some disciplines to continue studies that may
fail to prove profitable. When young people are approaching the end of their studies or when
their educational choices are irreversible, the costs related to changing their investment in
human capital may be too high. And the difficulties in finding work may come across as a
greater likelihood of being overeducated for the labour market.
In the economic literature, student surveys are often used to investigate how wage
expectations are constructed (Dominitz and Manski,1996; Betts, 1996; Brunello et al., 2004).
The purpose is to compare students’ expected earnings on the labour market with their career
plans or with existing jobs in accordance with certain characteristics of the young people:
their cognitive and non-cognitive abilities during their education, their socio-economic
background and the careers guidance they receive. From this perspective, the aim here is to
study the earnings expectations of first-year students at a French university and to compare
them with the observed earnings of young people in the labour market for that same year. In
both instances, the intended final qualification and the field of study are taken into account.
One of the original features of this research is that it includes, as explanatory variables,
several measurements of cognitive capacities which it might be thoughtwould influence
individuals’ expected earnings. Furthermore, although many surveys have been made in both
developed and developing countries, none has so far been conducted in Franceas far as we are
aware. This type of enquiry seems all the more important because the French labour market is
highly segmented to the disadvantage of young people. The structuring of internal markets
relegates young people to the position of outsiders on the labour market, an effect that is
further accentuated in times of economic crisis. It should be asked how young people in the
course of their education perceive these difficulties and how the difficulties might affect their
choices in terms of investment in human capital? These questions are all the more telling for
young people from more modest social backgrounds or from less-favoured areas, who often
tend to underestimate their future earnings and so cut back on their investment in education.
The first section of this paper proposes a survey of the empirical literature on the determinants
of expected earnings. The second section presents the data and the way the survey was
conducted. The third section explains the method with respect to earnings functions and
inaccurate estimations. The main empirical results are then set out and discussed in the fourth
section. The final section states the main conclusions and suggests some implications and
limitations.
1. Survey of the literature
A good deal of research has been done in education economics on the question of how
realistic earnings expectations are. While different empirical methods have been used to
measure young people’s earnings expectations, most research argues that expected wages are
overestimated compared with actual wages observed on the labour market. This is particularly
so when students begin their first year in higher education, even if the gap between the real
wage and the expected wage narrows during their time at university. Jerrim (2011) reports, for
example, that full time students in Britain overestimate their starting wage by about 15%
compared with wages observed on the labour market. Betts (1997) looks at another option by
2
questioning students at the University of California about starting payfor several occupations,
disciplines and levels of qualification. He concludes that expected average earnings are quite
close to actual wages, with mean errors of about 6% when overestimates and underestimates
for certain occupations are offset. In a rare example of research pointing to underestimations,
Menon et al. (2012), questioning young Cypriot students and graduates in a context of
economic crisis, report young people in the course of their studies underestimate their future
earnings. These different studies raise the question of the short-sightedness of expectations:
comparing expected earnings against actual earnings at the same date presupposes that the
individual fails to anticipate any change in terms of employment and earnings over the
medium term. Overestimating or underestimating earnings in the short term is not necessarily
overestimating or underestimating them in the long term because of changes in the labour
market and changes in wages, even if students might overlook phenomena like inflation
(Manski, 1993). On the basis of a longitudinal study, Weddink and Hartog (2004) circumvent
the question of short-sightedness by comparing expected earnings and actual earnings after
four years in the labour market for a sample of Dutch students. They conclude, even so, as in
most earlier studies, that earnings are overestimated: young people’s expected earnings are
about 10% higher than their actual earnings. Other studies compare the level of actual and
expected earnings of graduates and students from the same university so as to allow for bias
related to the quality of the institution, the characteristics of the students and their career
plans. After correction for these biases, Carjaval et al. (2000) also report that earnings are
slightly overestimated, with students not being fully informed about the returns to certain
characteristics on the labour market.
One of the main points of interest in the literature is the analys of the factors that lead young
people to over- or underestimate expected earnings, either on the basis of comparisons among
young people with different characteristics or by comparing expected and actual earnings for
given characteristics. According to the canonical model of human capital theory, those
characteristics depend largely on factors that will also influence the choice of field of study.
So the individual characteristics of young people and the conditions in which they form their
expectations are decisive. Williams and Gordon (1981), interviewing a sample of 16-year old
English schoolchildren, report that the intention of continuing into higher education is
associated with higher expected earnings. However, those earnings must be proportionately
higher when the young people are from modest social backgrounds or have lower cognitive
abilities.
Whatever the intended level of education, certain factors explain the expected differences
structurally. The earliest work emphasizes the different expectations of girls and boys (Smith
and Powell, 1990; Blau and Farber, 1991) with young men generally expecting higher wages
than young women, which is consistent with observed differences in earnings. Those
differences may nonetheless increase over the course of a career. Brunello et al. (2004)
reportthat girls expect their earnings to be about 9% lower at the end of their studies, but the
expected difference is twice as large 10 years after completing their education. In comparing
Swiss and US cases about expectations by gender, Wolter (2000) emphasizes that students
rationally make allowance for structural rigidities in the labour market.
Knowledge of the labour market is also an important differentiating factor. Expectations are
proportionally more specific as students approach the end of their education. For Jerrim
(2011), overestimations are twice as large for first-year students as for final-year students. He
points out, though, that students with paid part-time work hardly ever overestimate earnings:
the difference with actual earnings is of the order of 3%. Brunello et al. (2004) show that
3
older students expect lower earnings at the start of their careers. The same is also true for
students intending to undertake longer studies before graduating, which is consistent with
signal theory. However, those differences fade for expected earnings 10 years after entry into
the labour market.
The family socio-economic background is another factor explaining differences in
expectations (Smith and Powell, 1990). This may arise in the first place from family social
capital and better information about available employment on the labour market through their
parents. Streufert (2000), in a theoretical model, underscores the importance of genuine
diversity in the immediate social environment on educational choices: young people must be
able to observe all of the returns to education. Next, children from more favoured
backgrounds may also hope to benefit directly from those networks in their search for future
employment and so expect to be in the top part of the earnings distribution. Brunello et al.
(2004) thus show that the mother’s level of education has a positive influence on the expected
earnings of their children whereas the father’s level of education increases their expectation of
finding employment quickly. By contrast, what their parents studied in higher education does
not seem to influence expected earnings. The same is observed when the explanatory variable
is the parents’ occupation (Webbink and Hartog, 2004) or family income. Betts (1997)
reports, for example, that children whose parents are on modest incomes tend to make lower
estimates about their expected earnings. The study by Alonso-Borrego and Romero-Medina
(2010) of university students in Madrid pointslikewise to the young people with the higher
parental incomes expecting the highest remuneration. As Brunello et al. (2004) claim, this
access to information may also be had through formal informational channels about
employment in universities: students benefitting from such information generally expect
higher earnings. These social differences are compounded by differences relating to
membership of different minority groups, such as young people born abroad. Sometimes their
lack of information about the labour market in the host country may lead them to expect
higher earnings.
Educational abilities, whether self-assessed or measured by their past school performances,
are also decisive. Generally, young people who were the highest achievers in secondary
education or those with the highest cognitive abilities expect higher earnings for the same
intended level of education. This may be explained by their confidence in achieving their
study aims compared with others or by the fact that their cognitive capacities will enable
them, for an equal qualification, to more readily find employment in the top of the earnings
distribution. Brunello et al. (2004) show that students with better access to selective fields are
more likely to declare higher expected wages. The effect of field of study on expected
earnings is generally more contrasted. While it seems that students in the humanities do not
expect such high earnings, since it is often more complicated to find any occupational
opening, the results vary in different research work (Need and de Jong, 2008; Alonso-Borrego
and Romero-Medina, 2010).
Less readily observable variables like attitude towards risk or preference for the present,
which are decisive variables in human capital choices, may also explain differences in
expected earnings. Schweri et al. (2011)report that earnings expectationsfor a sample of Swiss
students are higher when wage variances in certain occupations are greater. This is consistent
with the findings of Williams and Gordon (1981), who also report that the students willing to
take more risks declare higher expected earnings. Results about preference for the present are
less clear-cut. In keeping with human capital theory, Williams and Gordon (1981) report that
young people who prefer to postpone rewards generally have higher earnings expectations,
4
and especially girls. Conversely, Brunello et al. (2004) observe, surprisingly, that young
people with a preference for the present expect higher wages. The study by Need and de Jong
(2008) of first-year Dutch students also emphasizes the importance of allowing for other
variables that can readily be observed such as character traits. Emotionally unstable young
people are those who predict, all else being equal, lower earnings, whereas those who look to
put their career first in future decisions between career and family hope for higher earnings.
2. Data
The survey was conducted in 2010 among first-year students at the University of Burgundy in
three fields of study: social and economic administration, psychology and law. It was
conducted in two stages. The first phase was designed to evaluate the abilities of students
before they began university. The survey, including tests to measure cognitive abilities, was
carried out in the week before the start of the 2010–2011 academic year, during which
students were called at random for a mandatory initiation week. The cognitive tests comprised
three speed processing tasks (Serial Reaction Time (SRT) and two Posner tasks), three
working memory tests (reading span, operation span and Time Based Resources Sharing
(TBRS)), and one reasoning task (the abridged form of Raven’s matrices). Academic ability
of high school students was measured by a written comprehension test. Lastly sociodemographic and economic data were collected at the start of the research. Some 220 students
took the various tests in the first phase. The second phase was in February 2011. Some 510
students completed a questionnaire during a lecture at which attendance was mandatory, some
of whom had already been questioned in phase 1. The questionnaire addressed their working
methods, any difficulties encountered in the course of the academic year, their past
educational record and their future educational plans. Information covering the two survey
phases was available for 169 students. In the second phase, students were asked two questions
about their expected earnings and their intended level of higher education. The questions
were:1
- What is the highest level of education you plan to achieve by the end of your higher
education?
- In your opinion, what will your net monthly earnings be one year after completing
higher education…, and 10 years after completing higher education?
The survey also included information about the students’ socio-demographic characteristics,
their career choices, their living conditions and their academic achievements. Sociodemographic characteristics were measured by social category and the father’s level of
education. Two other variables were constructed from the survey to try to determine how
much students knew about employment opportunities and the labour market.2 One variable
measured the geographical distance between the student’s high school and the university and
the other the closeness between the father’s occupation and the academic discipline chosen by
the student.
The students were also asked about their choice of orientation and more especially the reasons
for their choice of field of study, whether they had a career plan connected with their studies,
1
Unlike in other studies, such as Dominitz and Manski (1993), we were unable to ask students about different possible
earnings distributions, as such information requires computer processing, which was not possible for the second phase of the
survey.
2
A large proportion of students not from high schools close to the university were from rural areas where there is little skilled
employment.
5
the opinions of their parents and the people who had influenced their choice of orientation.
The conditions in which students lived and studied were ascertained through the type of halls
of residence, financial resources, and difficulties encountered while studying. Lastly, the
survey provided an objective measure of students’ academic achievement (examination
marks) and a subjective measure with students being asked to assess their own academic
level. Table 2B in Appendix B sets out the descriptive statistics for all of the variables.
Because of the way the survey was processed, information about certain variables was not
available for all students. The appended table specifies the frequencies for each variable.
6
Table 1: Descriptive statistics of earnings and number of years’ in higher education
Expected earnings
All
Men
Women
Raven’s matrices
Posner
Father had higher education
Father had secondary education
Mean
1613
1837
1511
1534
1588
1507
SD
543
658
454
425
538
311
1446
1592
1639
1699
1527
256
4611
475
623
403
Years in higher
education
Mean
SD
4.8
1.3
4.8
1.3
4.5
1.2
4.7
1.2
4.8
1.2
5.1
1.5
4.7
4.9
5
5.2
4.7
1.1
1.4
1.3
1.1
1.5
Table 1 provides a first look at the differences in educational levels and expected
earnings of first-year students. So as to be able to reason in terms of the rate of return to
education, we constructed a continuous variable of the student’s intended level of education.3
The average number of years of university education is approximately 4.8, corresponding to
mean earnings one year after entering the labour market of €1613. Expected earnings vary
markedly with individuals’ characteristics. The mean wage and intended number of years’
education seem to be lower for women than for men. Students whose fathers entered higher
education expect a mean remuneration of €1699 and 5.2 years in higher education versus
€1527 and 4.7 years for the others. Table 1 also reveals different expectations of
undergraduates depending on their cognitive abilities. Students who scored well on the
processing speed test (Posner) expected proportionately higher earnings, which does not seem
to be the case for reasoning capacity (Raven’s matrices). However, students with higher
scores for these two tests contemplate longer university education on average.
A national survey by Céreq in 2010 was used to compare actual earnings with expected
earnings of undergraduates at Burgundy University. The survey covered some 25,000 young
people having left the education system at all levels of qualification and questioned in 2010
about their insertion in the world of work, for their first three years of their working lives. The
survey can be used to ascertain the mean earnings of young people one year after leaving the
education system (in 2008) by sex, CITE level and discipline. In all, there is a sample of some
350 young graduates for the three disciplines and the different levels of qualification covered
by our survey.
3
17% of students planned to obtain a first degree, 68% a master’s degree, 8% a doctoral degree and 7% some
other level of higher education (BTS, IUT, etc.).
7
3. Method
First, we looked to measure the different determinants of expected earnings one year
after entering the labour market. Second, we measured the divergences in young people’s
expectations and tried to identify the various factors that might explain those divergences.
Earnings after one year
To analyse the characteristics explaining expected earnings, standard Mincer equation
were estimated. They were of the form:
where,
was the logarithm of the estimated wage after one year,
the number of
years in higher education and
a set of explanatory variables.
Now, it could be assumed that students anticipated the number of years in higher
education and the wage level together, which might entail endogeneity and simultaneity
biases. To allow for these biases, we also estimated simultaneous regressions by three-stage
least squares. This method allowed for simultaneity and was justified when the residuals of
the two equations might be correlated, which was probably the case here.
We estimated the equations:
where
and
were variables that were assumed to affect expected earnings and the
intended number of years in higher education, respectively.4
Given the survey structure, the estimations were performed first on the full sample
(510 students) and then on the subsample (169 students) comprising the information on
cognitive abilities.5 Because there were no instruments in the full sample, the simultaneous
equations were for the subsample alone.
Accuracy
In order to determine the divergence in estimations, we initially studied the earnings
observed from the Génération national survey. Mean observed earnings were calculated by
sex, discipline (administration, psychology, law) and level of education (first degree, master’s
degree, doctoral degree). The divergence in estimation could then be calculated from:
where
was the earnings the student expected and
the mean observed earnings
depending on the student’s characteristics (sex, discipline, intended level of education).
4
The instrumental variables for earnings are choice of orientation and geographical distance whereas, for the number of years
in higher education, they are type of accommodation and student’s financial resources.
5
To control possible selection bias we estimated a probitto control non responses between two samples (cf. annex C), which
enabled us to calculate a Mills inverse ratio. As this was not significant in the gains function, it seems there was no selection
bias.
8
The determinants of the misestimations were calculated by the same method as for
earnings one year after entry into the labour market, either by OLS or 3SLS.
4. Results
Earnings one year after completing higher education
Table 2 shows the results of OLS and 3SLS estimations for the two samples of students. First,
the effect of the number of years in higher education on expected earningsat labour market
entryis positive and significant, which is consistent with human capital theory. Returns on
education nonetheless vary from 3 to 4% for the OLS estimation to 10% for simultaneous
equation regressions. The unobserved characteristics increase both earnings and the number
of years in higher education, which leads to an underestimation of the net effect of the number
of years in higher education if it is not endogenized. However, expected earnings do not seem
to influence the intended number of years in higher education once earnings are endogenized.
No variable for secondary school performance turns out to be significant when the scores in
certain cognitive tests and the student’s self-assessment relative to school level are introduced.
Reasoning ability measured by Raven’s matrices and the perception of skills positively impact
the intended number of years in higher education. Conversely, only the information
processing speed,6 measured by the Posner test, seems to have a positive and significant
impact on expected earnings, which is consistent with a study by Heinek and Anger (2010)
highlighting a marked impact of cognitive speed on observed earnings.
6
Note: The indicators from these tests are response times in milliseconds, the best performances are therefore the lowest
values. The other tests were not included in the model as they were not significant.
9
Table 2: Determinants of earnings at 1 year, estimated by OLS and 2SLS
Full sample
Log of
earnings
OLS
Log of earnings
Number of years in higher education
Male
Fields of study
ref. Psychology
Law
Administration
Help with orientation in higher education
ref. No help
from parents
from friends
from others (careers guidance centre. etc.)
Self-assessed school performance
Ref. average or below
Above average
Ref. High school to university from 50 to 150 km
Distance <50
Distance >150
Cognitive tests
Raven’s matrices
Speed processing
Father’s social category
Father in managerial position (ref. other)
Link between father’s occupation and higher education
ref. father not in management and job unrelated to studies
Father in management and job related to studies
Father in management and job unrelated to studies
Father not in management and job related to studies
Career plan
ref. No career plan
Plan related to higher education
Plan unrelated to higher education
Parents’ opinion on orientation
Ref. Unfavourable or no opinion
Favourable opinion
Ground for university orientation
Ref. Other grounds
Choice by interest for subject
Extracurricular difficulties with effect on schooling
Family difficulty
Financial difficulty
Difficulty with daily travel time
Relational difficulties
Other difficulties
Residence (ref. not living with parents)
Living with parents
Main financial resources
ref. Financial resources related to paid activity
Grant student
Direct help from parents
Father’s level of education
Father had higher education
Left blank
R²
Sub-sample
Log of earnings
OLS
3SLS
0.03***
0.14***
0.04**
0.14***
0.18***
0.13***
0.06*
-0.08**
-0.03
OLS
0.86*
3 SLS
0.2
0.10**
0.15***
-0.10
0.01
0.11**
0.17**
0.095**
0.19***
0.43
0.39
0.13
-0.33
0.04
-0.11
-0.08
0.046
-0.11*
-0.09
0.51**
0.61**
0.05*
0.17*
0.02
0.05*
0.11***
Years in higher education
0.02
0.05
0.12***
0.07*
0.11**
-0.02
-0.03
-0.04*** -0.04***
0.04
0.09***
0.02
-0.00
-0.01
-0.03
-0.01
-0.12**
0.25
0 .15**
-0.03
0.08
0.15**
-0.04
0.07
0.07*
-0.03
0.06
0
0.10
-0.24
0.14
-0.28
0.11***
0.10***
0.08
0.16
-0.06
-0.08**
0.39**
0.37**
-0.01
0.02
0.05
0.05
-0.16*** -0.14***
0.07
0.06
0.15
0.11
-0.51*
-0.15
-0.11
0.11
0.34
-0.51**
-0.13
-0.21
0.16
0.51
-0.31
-0.42**
0.58**
0.14
0.5**
0.05
0.44*
-0.24
0.39*
0.31
0.30
0.41
0.30
-0.25
0.20
0.03
10
Generalized Hausman test7
N
510
169
Surprisingly, the father’s level of higher education and occupation have a fairly limited
influence on expected earnings, even if students whose fathers entered higher education
intended to undertake longer university studies. Having a father in a managerial position had
no direct effect on expected earnings. However, the connection between the father’s
occupation and the discipline chosen by young people whose parents were in managerial
positions had a 12% positive impact on expected earnings. This connection had no significant
effect for children from more modest social backgrounds. Furthermore, children of parents in
managerial positions but whose occupation is unrelated to their studies do not expect higher
earnings. These results show that the choice of orientation and the information students have
quite strongly structure their expectations about earnings. Students with a career plan related
to their higher education expect higher earnings. Similarly, students whose parents are in
favour of the higher education pursued also expect about 10% higher earnings. Contrariwise,
students whose choice of orientation depends essentially on their friends generally expect
lower earnings. In other words, the involvement of the families in the choice of higher
education leads young people to aim for higher levels of earnings, most certainly because of
better knowledge of the labour market. The distance between the student’s former high school
and the university has a significant effect, but is more ambiguous because it is not linear.
When the distance is less than 50 km, students expect slightly higher earnings perhaps
because of better knowledge of the skilled labour market in the largest city of the local region
or because it is possible to mobilize certain networks. Expected earnings are also higher for
students who did their secondary education in high schools far from the university (more than
150 km), which may be explained by them wanting to offset the greater cost of higher
education by higher earnings.
More classically, other variables like sex or field of study have pretty much expected effects
on the expected earnings. Women expect significantly lower earnings than men (about -14%),
which is consistent with earnings differentials observed on the labour market. The field of
study effect is important too. Students in law and administration expect significantly higher
earnings than those in psychology, a field in which it is reputedly more difficult to find
employment.
Lastly, students’ living conditions determine the intended number of years in higher education
but have no effect on earnings. Students whose main financial resource is their grant intend to
continue for longer in higher education. However, the contrary is found for students living
with their parents or having daily difficulties with travelling, certainly because of an adverse
effect on the hedonic preferences associated with their educational choice.
7
The Hausman test leads to accepting the endogeneity assumption.
11
Accuracy of expectations
Table 3 shows that generally first-year students overestimate their earnings upon entering the
labour market. The deviation is of about + 9% and is significant at the 1% level. This
deviation is similar to that observed in various studies of other countries: 6% in the United
States by Betts (1996) or 5.3% in the United States and in Switzerland by Wolter (2000). The
misestimate differs by sex, field of study and intended diploma. Men tend to overestimate
their starting salaries more than women (14.8% versus 5.9%). While the estimated error is not
significant for psychology students, law and administration students overestimate their
salaries by 18.5% and 10.3%, respectively. It seems that the misestimation is greater for
students intending to complete a master’s degree (11.9%) while it is relatively small for those
intending to do a first degree (2.5% significant at 5%) and not significant for those intending
to do a doctoral degree.
Table 3: Mean expected and observed salary one year after completing higher education
Expected salary Actual salary Difference test
All
1612.8
1474.7
9.4***
Men
1808.0
1575.0
14.8***
Women
1512.8
1429.1
5.9***
Law
1781.9
1503.2
18.5***
Psychology
1380.8
1434.5
-3.7
Administration
1581.7
1433.7
10.3***
First degree
1365.0
1332.0
2.5**
11.9***
Master’s
1679.0
1500.0
-4.7
Doctorate
1772.0
1860.0
Note: ***, **, * are for 1, 5 and 10%, respectively. Wilcoxom rank test.
Determinants of mistaken expectations
Table 4 shows the determinants of deviations in expectations for each sample by the OLS and
3SLS methods.
12
Table 4: Determinants of misestimations
Full sample
Misestimation
OLS
Misestimation
Number of years of study
Male
ref. Psychology
Law
Administration
ref. Orientation by self
By parents
By friends
Others
Assessment above average
ref. No career plan
Plan related to higher education
Plan unrelated to higher education
Father in managerial position
ref. Distance from 50 to 150 km
Distance < 50 km
Distance > 150 km
Parents’ opinion favourable
Raven’s matrices
Processing speed in seconds (*100)
Connection between father’s occupation and higher education
ref. father not in managerial position and unrelated to studies
Father in managerial position and related to studies
Father in managerial position and unrelated to studies
Father not in managerial position and related to studies
Choice by interest in subject
Family difficulty
Financial difficulty
Difficulty related to daily transport time
Relational difficulty
Other difficulty
Living with parents
ref. financial resources from work
main resources grant
main resources parents
Father had higher education
Left blank
R²
-0.03**
0.09***
0.21***
Sub-sample
Misestimation
Years of study
OLS
3SLS
3 SLS
0.22
-0.02
0.07
0.07
0.08
-0.04
0.11**
0.09*
0.21
0.10**
0.15*
0.17**
-0.31
0.07*
-0.06
-0.04
0.02
0.02
-0.11
-0.10
0.08*
0.02
-0.11
-0.10
0.03
0.51**
0.13***
0.02
0.04
0.10**
-0.02
0.01
0.07
0.01
-0.01
0.24
-0.17
0.09
0.06*
0.13***
-0.01
0.01
0.01
-0.04
-0.13*
0.07
0.09*
0.13**
0.13**
0.11** 0.11***
-0.01
-0.01*
-0.05*** -0.05***
0.19**
-0.02
0.12*
-0.06
-0.01
0.06
-0.16**
0.08
0.20*
0.19**
-0.04
0.11
-0.09*
0.04
0.07
-0.14**
0.07
0.16
0.02
0.05*
-0.18
0.18
-0.01
0.35*
-0.61*
-0.11
-0.17
0.16
0.39
-0.35*
0.45**
0.06
0.18
0.32
0.20
0.31
-0.08
0.24
First of all, the number of years’ in higher education has a negative impact on
divergences in expectations in the OLS estimates, which may be thought surprising. It may be
because undergraduates intending to obtain a doctoral degree tend to underestimate their
earnings. The result is not significant, though, in 3SLS estimates. More classically, men tend
to overestimate their earnings compared with women. Similarly, compared with other
disciplines, law students quite markedly overestimate their earnings (+21% compared with
psychology students), which may be because of the career prospects of law students who
intend to take up prestigious and well-paid occupations (attorneys, judiciary).
Career choices and careers information also have an impact on divergences in expectations.
Students whose parents approve their choice of higher education and whose studies are related
to their father’s occupation expect higher earnings than the observed means, by 11% and
16%, respectively. A similar result is observed for students whose career choice is connected
with the chosen course of study, although the coefficient becomes insignificant for 3SLS
methods. Conversely, it seems that students whose choice offield of study depends on their
13
liking or interest for the subject tend to underestimate their earnings (-9%). As in the previous
estimate, distance has a significant and non-linear impact on deviations. Students who went to
secondary school less than 50 km away and more than 150 km from the university tend to
overestimate their earnings. Lastly, students with the highest cognitive abilities in terms of
processing speed expect higher earnings than the average observed earnings. It may be that
they perceive themselves as having above average skills, which they will try to signal on the
labour market.
Expected earnings 10 years after completing higher education.
The survey also questioned undergraduates at the University of Burgundy about their
expected earnings 10 years after completing their higher education. These expected earnings
can be compared with actual earnings 10 years after labour market entry by the Céreq national
survey of people leaving higher education in 2008. As already observed in other research, the
misestimates compared with actual earnings increase when undergraduates are asked about a
more distant future occupation. The deviation in earnings 10 years on is 41%. Moreover, the
result is particularly striking for women: while it is just 29% for men, it exceeds 47% for
women. The results are also very variable by discipline and intended degree: divergences for
law students reach 69%. These results show that students have a much more approximate
knowledge of the labour market over the long term and of the yields of experience in work.
Conclusion
This work has sought to understand how first-year university students form expectations
about earnings as a function of their intended degree. The results, even if a slight
overestimation is observed, point to a certain proximity between expected earnings of young
people at the start of their careers and actual earnings on the labour market. Some categories
of young people, however, have higher expectations. We show the importance of the
environment in which students make their choices about education. Expected earnings are
proportionally higher when their parents seem to be involved in the careers guidance, even
controlling for the effect of the socio-economic background or the father’s level of education.
The positive opinion of parents about the orientation or the connection between the discipline
and the father’s occupation are generally associated with higher earnings. Conversely, having
chosen a course of studies out of a liking for it or further to peer advice seem to have an
adverse effect on expected earnings. The results point to a contrast between choices of higher
education made in the family context where the continuation in education is an investment
and a choice of field of study out of a liking for it where the educational consumption value
seems more important. Another point of interest has been to show the impact of cognitive
variables are far more significant than variables relating to past educational performances.
Young people seem attuned to the idea that having greater cognitive abilities, for the same
diplomas, will enable them to command higher earnings in their future occupational activity.
This research has several limitations. First, the size of the full sample, for which we have data
about cognitive abilities, is small and limited to three academic disciplines that are relatively
specific. Moreover, the deviations between expected earnings and actual earnings on the
labour market correspond to samples of students who are not necessarily graduates of the
same university even if they relate to the same diplomas and same disciplines. Lastly, because
of the form of survey, it was not possible to introduce variables such as attitude to risk which
generally have an effect on expected earnings. However, this research does underscore the
14
importance of allowing for background and the way young people construct their choices
about higher education. Those factors largely determine their educational ambitions.
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15
Appendix : Descriptive Statistics
Table 2A: Descriptive statistics of students surveyed
Male
Fields of study
Administration
Law
Psychology
Socio-economic background
Father’s social category
Father in managerial position
Father’s level of education
Father had higher education
Left blank
High school to university lower 50 km
From 50 to 150
Upper 150
Link between father’s occupation and higher education
Self-assessed school performance
Above average
Orientation
Choice by interest for subject
Favourable parents’ opinion on orientation
Help with orientation in higher education
from friends
from parents
from others (careers guidance centre. etc.)
Career plan
Plan related to higher education
Plan unrelated to higher education
Conditions in which students lived and studied
Residence
Living with parents
Main financial resources
Grant student
Extracurricular difficulties with effect on schooling
Family difficulty
Financial difficulty
Difficulty with daily travel time
Relational difficulties
Other difficulties
Statistics
32%
15,5%
52,2%
32,3%
Frequencies
510
510
24,5%
510
25%
6,5%
33,7%
46,9%
19,4%
17,7%
169
49,4%
510
47%
56%
169
169
8,8%
15%
9%
510
46%
13%
510
43%
510
29,7%
169
15,1%
15,3%
13,3%
8,6%
5,5%
510
510
169
16
Appendix: Probability of being in the first sample
Table 3A: Probit : Probability of being in the first sample
Variables
Constant
Male
Baccalauréat
réf. Baccalauréat of technology
S
ES
L
Baccalauréat with distinction
With honours
Fields of study ref. Psychology
Administration
Law
Self-assessed school performance
Above average
Father’s social category
Father in managerial position
Left blank
Grant student
Pseudo R²
N
Coef
-0,15
-0,27**
0,02
-0,02
-0,04
-0,08
-0,71***
-0,11
0,04
0,055
-0,01
0,02
0,033
513
17
First Degree
0
1000
2000
3000
4000
0
0
.0005
.0005
Density
.001
Density
.001
.0015
.002
.0015
Appendix : Distribution of expected and observed wages
5000
Wage
0
women's observed wages
women's expected wage
1000
2000
3000
4000
5000
Wage
men's observed wages
men's expected wages
expected wage
Doctorate
0
0
Density
.0005
Density
.0005
.001
.001
Master's
observed wage
0
1000
2000
3000
4000
5000
1000
2000
3000
Salaire
4000
Wage
expected wage
observed wage
expected wage
Administration
0
0
.0005
Density
.0005
Density
.001
.001
.0015
Law
observed wage
0
1000
2000
3000
4000
5000
wage
0
1000
2000
3000
4000
5000
Wage
expected wage
observed wage
expected wage
observed wage
0
.0005
Density
.001
.0015
.002
Psychology
0
1000
2000
3000
Wage
expected wage
observed wage
18